January 2018

Releases are staged. Your Databricks account may not be updated until a week after the initial release date.

Note

We are now providing Databricks Runtime deprecation notices in the Databricks Runtime Release Notes.

Mount points for Azure Blob Storage containers and Data Lake Stores

Jan 16-23, 2018: Version 2.63

We have provided instructions for mounting Azure Blob Storage containers and Data Lake Stores through the Databricks File System (DBFS). This gives all users in the same workspace the ability to access the Blob Storage container or Data Lake Store (or folder inside the container or store) through the mount point. DBFS manages the credentials used to access a mounted Blob Storage container or Data Lake Store and automatically handles the authentication with Azure Blob Storage or Data Lake Store in the background.

Note that mounting Blob Storage containers and Data Lake Stores requires Databricks Runtime 4.0 and above. Once a container or store is mounted, you can use Runtime 3.4 or above to access the mount point.

See Azure Blob Storage and Azure Data Lake Storage Gen1 for more information.

Table Access Control for SQL and Python (Private Preview)

Jan 4-11, 2018: Version 2.62

Note

This feature is in private preview. Please contact your account manager to request access. This feature also requires Databricks Runtime 3.5+.

Last year, we introduced data object access control for SQL users. Today we are excited to announce the private preview of Table Access Control (ACL) for both SQL and Python users. With Table Access Control, you can restrict access to securable objects like tables, databases, views, or functions. You can also provide fine-grained access control (to rows and columns matching specific conditions, for example) by setting permissions on derived views containing arbitrary queries.

See Table Access Control for more information.

Exporting notebook job run results via API

Jan 4-11, 2018: Version 2.62

To improve your ability to share and collaborate on the results of jobs, we now have a new Jobs API endpoint, jobs/runs/export that lets you retrieve the static HTML representation of a notebook job’s run results in both code and dashboard view.

See Runs Export for more information.

Apache Airflow 1.9.0 includes Databricks integration

Jan 2, 2018

Last year, we released a preview feature in Airflow – a popular solution for managing ETL scheduling – that allows customers to natively create tasks that trigger Databricks runs in an Airflow DAG. We’re pleased to announce that these integrations have been released publicly in the 1.9.0 release of Airflow.

See Apache Airflow for more information.